Li, X, Li, K orcid.org/0000-0001-6657-0522, Yang, Z et al. (1 more author) (2017) A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries. In: Li, K, Xue, Y, Cui, S, Niu, Q, Yang, Z and Luk, P, (eds.) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. LSMS 2017: International Conference on Life System Modeling and Simulation and ICSEE 2017: International Conference on Intelligent Computing for Sustainable Energy and Environment, 22-24 Sep 2017, Nanjing, China. Springer Verlag , pp. 386-395. ISBN 978-981-10-6363-3
Abstract
As a popular type of Redox Flow Batteries (RFBs), single flow Zinc Nickel Battery (ZNB) was proposed in the last decade without requiring an expensive and complex ionic membrane in the battery. In this paper, a Radial Basis Function (RBF) neural model is proposed for modelling the behaviours of ZNBs. Both the linear and non-linear parameters in the model are tuned through a new feedback-learning phase assisted Teaching-Learning-Based Optimization (TLBO) method. Besides, the fast recursive algorithm (FRA) is applied to select the proper inputs and network structure to reduce the modelling error and computational efforts. The experimental results confirm that the proposed methods are capable of producing ZNB models with desirable performance over both training and test data.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Keywords: | Zinc Nickel Batteries (ZNBs); Radial Basis Function (RBF); Teaching-Learning-Feedback-Based Optimization (TLFBO) |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2018 10:55 |
Last Modified: | 06 Mar 2019 14:21 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-981-10-6364-0_39 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139096 |